TY  - JOUR
T1  - A Decision Support System on Artificial Intelligence Based Early Diagnosis of Sepsis
AU  - Kaya Aksoy, Pınar
AU  - Erdemir, Fatih
AU  - Kılınç, Deniz
AU  - Er, Orhan
PY  - 2022
DA  - April
JF  - Artificial Intelligence Theory and Applications
JO  - AITA
PB  - İzmir Bakırçay Üniversitesi
WT  - DergiPark
SN  - 2757-9778
SP  - 14
EP  - 26
VL  - 2
IS  - 1
LA  - en
AB  - Sepsis is the intense reaction of the immune system as a result of a severe infection in any part of the body and damages to organs and tissues. And this disease is commonly fatal and costly. In this study, we perform a comparative study for Sepsis prediction using machine learning algorithms from original laboratory findings. For this purpose, thirty-two different machine learning algorithms including different  tructures as well as neural network classifiers are evaluated and compared. As a result of experimental studies, SVM (Cubic, Fine Gaussian), KNN (Fine, Weighted, Subspace), Trees (Weighted, Boosted, Bagged) and neural network-based classifiers have achieved a significant success rate in the diagnosis of Sepsis using the new dataset. Thus, it is concluded that it is appropriate to use machine learning algorithms to predict whether a Sepsis patient will be survived. This study has the potential to be used as a new supportive tool for doctors when predicting Sepsis.
KW  - sepsis
KW  - early forecasting
KW  - artificial intelligence
KW  - decision support systems
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UR  - https://dergipark.org.tr/en/pub/aita/issue//1136215
L1  - https://dergipark.org.tr/en/download/article-file/2509018
ER  -